Intro R
Tim Dennis
10/5/2020
Episode data types
- Download and read file in
download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_data.csv", destfile = "data/gapminder_data.csv")
gapminder <- read.csv("data/gapminder_data.csv")We can also read in file from web
gapminder <- read.csv("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_data.csv")- What do we have, we can run the
str()function on any R object:
str(gapminder) #str stands for structure ## 'data.frame': 1704 obs. of 6 variables:
## $ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ pop : num 8425333 9240934 10267083 11537966 13079460 ...
## $ continent: chr "Asia" "Asia" "Asia" "Asia" ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ gdpPercap: num 779 821 853 836 740 ...- We can get it to open up in a tab in RStudio a couple of ways:
View(gapminder) #to look at it in a tab Data types
R has a few data types it is good be aware of:
typeof(gapminder$year)## [1] "integer"typeof(gapminder$lifeExp)## [1] "double"typeof(3.14)## [1] "double"typeof(TRUE) # logical ## [1] "logical"typeof(1L) # The L suffix forces the number to be an integer, since by default R uses float numbers## [1] "integer"typeof('bannana')## [1] "character"class(gapminder)## [1] "data.frame"typeof(gapminder$continent)## [1] "character"typeof(gapminder$country) #character## [1] "character"- No matter how complicated our analyses become, all data in R is interpreted as one of these basic data types.
- This strictness has some really important consequences.
typeof(gapminder$year)## [1] "integer"Vectors
- Let’s step back and talk a bit about vectors.
- Dataframes are a collection of vectors
- Vectors can only be of one type.
- When we create vectors this has important consequences - R will coerce types to be the same.
- lets create some vectors:
x <- c(1, 2.4, 3, 5) #what's the <- again?
x## [1] 1.0 2.4 3.0 5.0str(x)## num [1:4] 1 2.4 3 5typeof(x)## [1] "double"Couple of things * the c() function is used in R a lot - stands for combine and it will create a vector * there are other ways to create a vector but we use this a lot. * what happens if we create a mixed vector
y = c("dog", 1.4, 3.5, TRUE)
y## [1] "dog" "1.4" "3.5" "TRUE"str(y)## chr [1:4] "dog" "1.4" "3.5" "TRUE"typeof(y)## [1] "character"- What happened?
- Yes, R converted this vector to all characters, but quotes around them.
- This is b/c it wouldn’t know how to convert dog or TRUE to numbers.
- The coercion rules go: logical -> integer -> numeric -> complex -> character, where -> can be read as are transformed into.
- R has a number of functions that converts vectors between types, sometimes we need to do this, esp. if on read you data is something not expected.
as.numeric,as.logical,as.character
char_vector_nums <- c('1','2','3')typeof(as.numeric(char_vector_nums))## [1] "double"Data structures = realistic example
str(gapminder)## 'data.frame': 1704 obs. of 6 variables:
## $ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ pop : num 8425333 9240934 10267083 11537966 13079460 ...
## $ continent: chr "Asia" "Asia" "Asia" "Asia" ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ gdpPercap: num 779 821 853 836 740 ...summary(gapminder$country)## Length Class Mode
## 1704 character character- What happened here?
- Let’s look up some help? What should i Run?
?summarysummary(gapminder$country) # tell us lenght of character vector ## Length Class Mode
## 1704 character character- But we want a diff summary, we’d lke to count up the occurances of our country.
- To do this we need to convert the country vector to a factor. What’s a factor? categorical variable, e.g. colors, breeds of dogs
factor(gapminder$country)## [1] Afghanistan Afghanistan
## [3] Afghanistan Afghanistan
## [5] Afghanistan Afghanistan
## [7] Afghanistan Afghanistan
## [9] Afghanistan Afghanistan
## [11] Afghanistan Afghanistan
## [13] Albania Albania
## [15] Albania Albania
## [17] Albania Albania
## [19] Albania Albania
## [21] Albania Albania
## [23] Albania Albania
## [25] Algeria Algeria
## [27] Algeria Algeria
## [29] Algeria Algeria
## [31] Algeria Algeria
## [33] Algeria Algeria
## [35] Algeria Algeria
## [37] Angola Angola
## [39] Angola Angola
## [41] Angola Angola
## [43] Angola Angola
## [45] Angola Angola
## [47] Angola Angola
## [49] Argentina Argentina
## [51] Argentina Argentina
## [53] Argentina Argentina
## [55] Argentina Argentina
## [57] Argentina Argentina
## [59] Argentina Argentina
## [61] Australia Australia
## [63] Australia Australia
## [65] Australia Australia
## [67] Australia Australia
## [69] Australia Australia
## [71] Australia Australia
## [73] Austria Austria
## [75] Austria Austria
## [77] Austria Austria
## [79] Austria Austria
## [81] Austria Austria
## [83] Austria Austria
## [85] Bahrain Bahrain
## [87] Bahrain Bahrain
## [89] Bahrain Bahrain
## [91] Bahrain Bahrain
## [93] Bahrain Bahrain
## [95] Bahrain Bahrain
## [97] Bangladesh Bangladesh
## [99] Bangladesh Bangladesh
## [101] Bangladesh Bangladesh
## [103] Bangladesh Bangladesh
## [105] Bangladesh Bangladesh
## [107] Bangladesh Bangladesh
## [109] Belgium Belgium
## [111] Belgium Belgium
## [113] Belgium Belgium
## [115] Belgium Belgium
## [117] Belgium Belgium
## [119] Belgium Belgium
## [121] Benin Benin
## [123] Benin Benin
## [125] Benin Benin
## [127] Benin Benin
## [129] Benin Benin
## [131] Benin Benin
## [133] Bolivia Bolivia
## [135] Bolivia Bolivia
## [137] Bolivia Bolivia
## [139] Bolivia Bolivia
## [141] Bolivia Bolivia
## [143] Bolivia Bolivia
## [145] Bosnia and Herzegovina Bosnia and Herzegovina
## [147] Bosnia and Herzegovina Bosnia and Herzegovina
## [149] Bosnia and Herzegovina Bosnia and Herzegovina
## [151] Bosnia and Herzegovina Bosnia and Herzegovina
## [153] Bosnia and Herzegovina Bosnia and Herzegovina
## [155] Bosnia and Herzegovina Bosnia and Herzegovina
## [157] Botswana Botswana
## [159] Botswana Botswana
## [161] Botswana Botswana
## [163] Botswana Botswana
## [165] Botswana Botswana
## [167] Botswana Botswana
## [169] Brazil Brazil
## [171] Brazil Brazil
## [173] Brazil Brazil
## [175] Brazil Brazil
## [177] Brazil Brazil
## [179] Brazil Brazil
## [181] Bulgaria Bulgaria
## [183] Bulgaria Bulgaria
## [185] Bulgaria Bulgaria
## [187] Bulgaria Bulgaria
## [189] Bulgaria Bulgaria
## [191] Bulgaria Bulgaria
## [193] Burkina Faso Burkina Faso
## [195] Burkina Faso Burkina Faso
## [197] Burkina Faso Burkina Faso
## [199] Burkina Faso Burkina Faso
## [201] Burkina Faso Burkina Faso
## [203] Burkina Faso Burkina Faso
## [205] Burundi Burundi
## [207] Burundi Burundi
## [209] Burundi Burundi
## [211] Burundi Burundi
## [213] Burundi Burundi
## [215] Burundi Burundi
## [217] Cambodia Cambodia
## [219] Cambodia Cambodia
## [221] Cambodia Cambodia
## [223] Cambodia Cambodia
## [225] Cambodia Cambodia
## [227] Cambodia Cambodia
## [229] Cameroon Cameroon
## [231] Cameroon Cameroon
## [233] Cameroon Cameroon
## [235] Cameroon Cameroon
## [237] Cameroon Cameroon
## [239] Cameroon Cameroon
## [241] Canada Canada
## [243] Canada Canada
## [245] Canada Canada
## [247] Canada Canada
## [249] Canada Canada
## [251] Canada Canada
## [253] Central African Republic Central African Republic
## [255] Central African Republic Central African Republic
## [257] Central African Republic Central African Republic
## [259] Central African Republic Central African Republic
## [261] Central African Republic Central African Republic
## [263] Central African Republic Central African Republic
## [265] Chad Chad
## [267] Chad Chad
## [269] Chad Chad
## [271] Chad Chad
## [273] Chad Chad
## [275] Chad Chad
## [277] Chile Chile
## [279] Chile Chile
## [281] Chile Chile
## [283] Chile Chile
## [285] Chile Chile
## [287] Chile Chile
## [289] China China
## [291] China China
## [293] China China
## [295] China China
## [297] China China
## [299] China China
## [301] Colombia Colombia
## [303] Colombia Colombia
## [305] Colombia Colombia
## [307] Colombia Colombia
## [309] Colombia Colombia
## [311] Colombia Colombia
## [313] Comoros Comoros
## [315] Comoros Comoros
## [317] Comoros Comoros
## [319] Comoros Comoros
## [321] Comoros Comoros
## [323] Comoros Comoros
## [325] Congo Dem. Rep. Congo Dem. Rep.
## [327] Congo Dem. Rep. Congo Dem. Rep.
## [329] Congo Dem. Rep. Congo Dem. Rep.
## [331] Congo Dem. Rep. Congo Dem. Rep.
## [333] Congo Dem. Rep. Congo Dem. Rep.
## [335] Congo Dem. Rep. Congo Dem. Rep.
## [337] Congo Rep. Congo Rep.
## [339] Congo Rep. Congo Rep.
## [341] Congo Rep. Congo Rep.
## [343] Congo Rep. Congo Rep.
## [345] Congo Rep. Congo Rep.
## [347] Congo Rep. Congo Rep.
## [349] Costa Rica Costa Rica
## [351] Costa Rica Costa Rica
## [353] Costa Rica Costa Rica
## [355] Costa Rica Costa Rica
## [357] Costa Rica Costa Rica
## [359] Costa Rica Costa Rica
## [361] Cote d'Ivoire Cote d'Ivoire
## [363] Cote d'Ivoire Cote d'Ivoire
## [365] Cote d'Ivoire Cote d'Ivoire
## [367] Cote d'Ivoire Cote d'Ivoire
## [369] Cote d'Ivoire Cote d'Ivoire
## [371] Cote d'Ivoire Cote d'Ivoire
## [373] Croatia Croatia
## [375] Croatia Croatia
## [377] Croatia Croatia
## [379] Croatia Croatia
## [381] Croatia Croatia
## [383] Croatia Croatia
## [385] Cuba Cuba
## [387] Cuba Cuba
## [389] Cuba Cuba
## [391] Cuba Cuba
## [393] Cuba Cuba
## [395] Cuba Cuba
## [397] Czech Republic Czech Republic
## [399] Czech Republic Czech Republic
## [401] Czech Republic Czech Republic
## [403] Czech Republic Czech Republic
## [405] Czech Republic Czech Republic
## [407] Czech Republic Czech Republic
## [409] Denmark Denmark
## [411] Denmark Denmark
## [413] Denmark Denmark
## [415] Denmark Denmark
## [417] Denmark Denmark
## [419] Denmark Denmark
## [421] Djibouti Djibouti
## [423] Djibouti Djibouti
## [425] Djibouti Djibouti
## [427] Djibouti Djibouti
## [429] Djibouti Djibouti
## [431] Djibouti Djibouti
## [433] Dominican Republic Dominican Republic
## [435] Dominican Republic Dominican Republic
## [437] Dominican Republic Dominican Republic
## [439] Dominican Republic Dominican Republic
## [441] Dominican Republic Dominican Republic
## [443] Dominican Republic Dominican Republic
## [445] Ecuador Ecuador
## [447] Ecuador Ecuador
## [449] Ecuador Ecuador
## [451] Ecuador Ecuador
## [453] Ecuador Ecuador
## [455] Ecuador Ecuador
## [457] Egypt Egypt
## [459] Egypt Egypt
## [461] Egypt Egypt
## [463] Egypt Egypt
## [465] Egypt Egypt
## [467] Egypt Egypt
## [469] El Salvador El Salvador
## [471] El Salvador El Salvador
## [473] El Salvador El Salvador
## [475] El Salvador El Salvador
## [477] El Salvador El Salvador
## [479] El Salvador El Salvador
## [481] Equatorial Guinea Equatorial Guinea
## [483] Equatorial Guinea Equatorial Guinea
## [485] Equatorial Guinea Equatorial Guinea
## [487] Equatorial Guinea Equatorial Guinea
## [489] Equatorial Guinea Equatorial Guinea
## [491] Equatorial Guinea Equatorial Guinea
## [493] Eritrea Eritrea
## [495] Eritrea Eritrea
## [497] Eritrea Eritrea
## [499] Eritrea Eritrea
## [501] Eritrea Eritrea
## [503] Eritrea Eritrea
## [505] Ethiopia Ethiopia
## [507] Ethiopia Ethiopia
## [509] Ethiopia Ethiopia
## [511] Ethiopia Ethiopia
## [513] Ethiopia Ethiopia
## [515] Ethiopia Ethiopia
## [517] Finland Finland
## [519] Finland Finland
## [521] Finland Finland
## [523] Finland Finland
## [525] Finland Finland
## [527] Finland Finland
## [529] France France
## [531] France France
## [533] France France
## [535] France France
## [537] France France
## [539] France France
## [541] Gabon Gabon
## [543] Gabon Gabon
## [545] Gabon Gabon
## [547] Gabon Gabon
## [549] Gabon Gabon
## [551] Gabon Gabon
## [553] Gambia Gambia
## [555] Gambia Gambia
## [557] Gambia Gambia
## [559] Gambia Gambia
## [561] Gambia Gambia
## [563] Gambia Gambia
## [565] Germany Germany
## [567] Germany Germany
## [569] Germany Germany
## [571] Germany Germany
## [573] Germany Germany
## [575] Germany Germany
## [577] Ghana Ghana
## [579] Ghana Ghana
## [581] Ghana Ghana
## [583] Ghana Ghana
## [585] Ghana Ghana
## [587] Ghana Ghana
## [589] Greece Greece
## [591] Greece Greece
## [593] Greece Greece
## [595] Greece Greece
## [597] Greece Greece
## [599] Greece Greece
## [601] Guatemala Guatemala
## [603] Guatemala Guatemala
## [605] Guatemala Guatemala
## [607] Guatemala Guatemala
## [609] Guatemala Guatemala
## [611] Guatemala Guatemala
## [613] Guinea Guinea
## [615] Guinea Guinea
## [617] Guinea Guinea
## [619] Guinea Guinea
## [621] Guinea Guinea
## [623] Guinea Guinea
## [625] Guinea-Bissau Guinea-Bissau
## [627] Guinea-Bissau Guinea-Bissau
## [629] Guinea-Bissau Guinea-Bissau
## [631] Guinea-Bissau Guinea-Bissau
## [633] Guinea-Bissau Guinea-Bissau
## [635] Guinea-Bissau Guinea-Bissau
## [637] Haiti Haiti
## [639] Haiti Haiti
## [641] Haiti Haiti
## [643] Haiti Haiti
## [645] Haiti Haiti
## [647] Haiti Haiti
## [649] Honduras Honduras
## [651] Honduras Honduras
## [653] Honduras Honduras
## [655] Honduras Honduras
## [657] Honduras Honduras
## [659] Honduras Honduras
## [661] Hong Kong China Hong Kong China
## [663] Hong Kong China Hong Kong China
## [665] Hong Kong China Hong Kong China
## [667] Hong Kong China Hong Kong China
## [669] Hong Kong China Hong Kong China
## [671] Hong Kong China Hong Kong China
## [673] Hungary Hungary
## [675] Hungary Hungary
## [677] Hungary Hungary
## [679] Hungary Hungary
## [681] Hungary Hungary
## [683] Hungary Hungary
## [685] Iceland Iceland
## [687] Iceland Iceland
## [689] Iceland Iceland
## [691] Iceland Iceland
## [693] Iceland Iceland
## [695] Iceland Iceland
## [697] India India
## [699] India India
## [701] India India
## [703] India India
## [705] India India
## [707] India India
## [709] Indonesia Indonesia
## [711] Indonesia Indonesia
## [713] Indonesia Indonesia
## [715] Indonesia Indonesia
## [717] Indonesia Indonesia
## [719] Indonesia Indonesia
## [721] Iran Iran
## [723] Iran Iran
## [725] Iran Iran
## [727] Iran Iran
## [729] Iran Iran
## [731] Iran Iran
## [733] Iraq Iraq
## [735] Iraq Iraq
## [737] Iraq Iraq
## [739] Iraq Iraq
## [741] Iraq Iraq
## [743] Iraq Iraq
## [745] Ireland Ireland
## [747] Ireland Ireland
## [749] Ireland Ireland
## [751] Ireland Ireland
## [753] Ireland Ireland
## [755] Ireland Ireland
## [757] Israel Israel
## [759] Israel Israel
## [761] Israel Israel
## [763] Israel Israel
## [765] Israel Israel
## [767] Israel Israel
## [769] Italy Italy
## [771] Italy Italy
## [773] Italy Italy
## [775] Italy Italy
## [777] Italy Italy
## [779] Italy Italy
## [781] Jamaica Jamaica
## [783] Jamaica Jamaica
## [785] Jamaica Jamaica
## [787] Jamaica Jamaica
## [789] Jamaica Jamaica
## [791] Jamaica Jamaica
## [793] Japan Japan
## [795] Japan Japan
## [797] Japan Japan
## [799] Japan Japan
## [801] Japan Japan
## [803] Japan Japan
## [805] Jordan Jordan
## [807] Jordan Jordan
## [809] Jordan Jordan
## [811] Jordan Jordan
## [813] Jordan Jordan
## [815] Jordan Jordan
## [817] Kenya Kenya
## [819] Kenya Kenya
## [821] Kenya Kenya
## [823] Kenya Kenya
## [825] Kenya Kenya
## [827] Kenya Kenya
## [829] Korea Dem. Rep. Korea Dem. Rep.
## [831] Korea Dem. Rep. Korea Dem. Rep.
## [833] Korea Dem. Rep. Korea Dem. Rep.
## [835] Korea Dem. Rep. Korea Dem. Rep.
## [837] Korea Dem. Rep. Korea Dem. Rep.
## [839] Korea Dem. Rep. Korea Dem. Rep.
## [841] Korea Rep. Korea Rep.
## [843] Korea Rep. Korea Rep.
## [845] Korea Rep. Korea Rep.
## [847] Korea Rep. Korea Rep.
## [849] Korea Rep. Korea Rep.
## [851] Korea Rep. Korea Rep.
## [853] Kuwait Kuwait
## [855] Kuwait Kuwait
## [857] Kuwait Kuwait
## [859] Kuwait Kuwait
## [861] Kuwait Kuwait
## [863] Kuwait Kuwait
## [865] Lebanon Lebanon
## [867] Lebanon Lebanon
## [869] Lebanon Lebanon
## [871] Lebanon Lebanon
## [873] Lebanon Lebanon
## [875] Lebanon Lebanon
## [877] Lesotho Lesotho
## [879] Lesotho Lesotho
## [881] Lesotho Lesotho
## [883] Lesotho Lesotho
## [885] Lesotho Lesotho
## [887] Lesotho Lesotho
## [889] Liberia Liberia
## [891] Liberia Liberia
## [893] Liberia Liberia
## [895] Liberia Liberia
## [897] Liberia Liberia
## [899] Liberia Liberia
## [901] Libya Libya
## [903] Libya Libya
## [905] Libya Libya
## [907] Libya Libya
## [909] Libya Libya
## [911] Libya Libya
## [913] Madagascar Madagascar
## [915] Madagascar Madagascar
## [917] Madagascar Madagascar
## [919] Madagascar Madagascar
## [921] Madagascar Madagascar
## [923] Madagascar Madagascar
## [925] Malawi Malawi
## [927] Malawi Malawi
## [929] Malawi Malawi
## [931] Malawi Malawi
## [933] Malawi Malawi
## [935] Malawi Malawi
## [937] Malaysia Malaysia
## [939] Malaysia Malaysia
## [941] Malaysia Malaysia
## [943] Malaysia Malaysia
## [945] Malaysia Malaysia
## [947] Malaysia Malaysia
## [949] Mali Mali
## [951] Mali Mali
## [953] Mali Mali
## [955] Mali Mali
## [957] Mali Mali
## [959] Mali Mali
## [961] Mauritania Mauritania
## [963] Mauritania Mauritania
## [965] Mauritania Mauritania
## [967] Mauritania Mauritania
## [969] Mauritania Mauritania
## [971] Mauritania Mauritania
## [973] Mauritius Mauritius
## [975] Mauritius Mauritius
## [977] Mauritius Mauritius
## [979] Mauritius Mauritius
## [981] Mauritius Mauritius
## [983] Mauritius Mauritius
## [985] Mexico Mexico
## [987] Mexico Mexico
## [989] Mexico Mexico
## [991] Mexico Mexico
## [993] Mexico Mexico
## [995] Mexico Mexico
## [997] Mongolia Mongolia
## [999] Mongolia Mongolia
## [1001] Mongolia Mongolia
## [1003] Mongolia Mongolia
## [1005] Mongolia Mongolia
## [1007] Mongolia Mongolia
## [1009] Montenegro Montenegro
## [1011] Montenegro Montenegro
## [1013] Montenegro Montenegro
## [1015] Montenegro Montenegro
## [1017] Montenegro Montenegro
## [1019] Montenegro Montenegro
## [1021] Morocco Morocco
## [1023] Morocco Morocco
## [1025] Morocco Morocco
## [1027] Morocco Morocco
## [1029] Morocco Morocco
## [1031] Morocco Morocco
## [1033] Mozambique Mozambique
## [1035] Mozambique Mozambique
## [1037] Mozambique Mozambique
## [1039] Mozambique Mozambique
## [1041] Mozambique Mozambique
## [1043] Mozambique Mozambique
## [1045] Myanmar Myanmar
## [1047] Myanmar Myanmar
## [1049] Myanmar Myanmar
## [1051] Myanmar Myanmar
## [1053] Myanmar Myanmar
## [1055] Myanmar Myanmar
## [1057] Namibia Namibia
## [1059] Namibia Namibia
## [1061] Namibia Namibia
## [1063] Namibia Namibia
## [1065] Namibia Namibia
## [1067] Namibia Namibia
## [1069] Nepal Nepal
## [1071] Nepal Nepal
## [1073] Nepal Nepal
## [1075] Nepal Nepal
## [1077] Nepal Nepal
## [1079] Nepal Nepal
## [1081] Netherlands Netherlands
## [1083] Netherlands Netherlands
## [1085] Netherlands Netherlands
## [1087] Netherlands Netherlands
## [1089] Netherlands Netherlands
## [1091] Netherlands Netherlands
## [1093] New Zealand New Zealand
## [1095] New Zealand New Zealand
## [1097] New Zealand New Zealand
## [1099] New Zealand New Zealand
## [1101] New Zealand New Zealand
## [1103] New Zealand New Zealand
## [1105] Nicaragua Nicaragua
## [1107] Nicaragua Nicaragua
## [1109] Nicaragua Nicaragua
## [1111] Nicaragua Nicaragua
## [1113] Nicaragua Nicaragua
## [1115] Nicaragua Nicaragua
## [1117] Niger Niger
## [1119] Niger Niger
## [1121] Niger Niger
## [1123] Niger Niger
## [1125] Niger Niger
## [1127] Niger Niger
## [1129] Nigeria Nigeria
## [1131] Nigeria Nigeria
## [1133] Nigeria Nigeria
## [1135] Nigeria Nigeria
## [1137] Nigeria Nigeria
## [1139] Nigeria Nigeria
## [1141] Norway Norway
## [1143] Norway Norway
## [1145] Norway Norway
## [1147] Norway Norway
## [1149] Norway Norway
## [1151] Norway Norway
## [1153] Oman Oman
## [1155] Oman Oman
## [1157] Oman Oman
## [1159] Oman Oman
## [1161] Oman Oman
## [1163] Oman Oman
## [1165] Pakistan Pakistan
## [1167] Pakistan Pakistan
## [1169] Pakistan Pakistan
## [1171] Pakistan Pakistan
## [1173] Pakistan Pakistan
## [1175] Pakistan Pakistan
## [1177] Panama Panama
## [1179] Panama Panama
## [1181] Panama Panama
## [1183] Panama Panama
## [1185] Panama Panama
## [1187] Panama Panama
## [1189] Paraguay Paraguay
## [1191] Paraguay Paraguay
## [1193] Paraguay Paraguay
## [1195] Paraguay Paraguay
## [1197] Paraguay Paraguay
## [1199] Paraguay Paraguay
## [1201] Peru Peru
## [1203] Peru Peru
## [1205] Peru Peru
## [1207] Peru Peru
## [1209] Peru Peru
## [1211] Peru Peru
## [1213] Philippines Philippines
## [1215] Philippines Philippines
## [1217] Philippines Philippines
## [1219] Philippines Philippines
## [1221] Philippines Philippines
## [1223] Philippines Philippines
## [1225] Poland Poland
## [1227] Poland Poland
## [1229] Poland Poland
## [1231] Poland Poland
## [1233] Poland Poland
## [1235] Poland Poland
## [1237] Portugal Portugal
## [1239] Portugal Portugal
## [1241] Portugal Portugal
## [1243] Portugal Portugal
## [1245] Portugal Portugal
## [1247] Portugal Portugal
## [1249] Puerto Rico Puerto Rico
## [1251] Puerto Rico Puerto Rico
## [1253] Puerto Rico Puerto Rico
## [1255] Puerto Rico Puerto Rico
## [1257] Puerto Rico Puerto Rico
## [1259] Puerto Rico Puerto Rico
## [1261] Reunion Reunion
## [1263] Reunion Reunion
## [1265] Reunion Reunion
## [1267] Reunion Reunion
## [1269] Reunion Reunion
## [1271] Reunion Reunion
## [1273] Romania Romania
## [1275] Romania Romania
## [1277] Romania Romania
## [1279] Romania Romania
## [1281] Romania Romania
## [1283] Romania Romania
## [1285] Rwanda Rwanda
## [1287] Rwanda Rwanda
## [1289] Rwanda Rwanda
## [1291] Rwanda Rwanda
## [1293] Rwanda Rwanda
## [1295] Rwanda Rwanda
## [1297] Sao Tome and Principe Sao Tome and Principe
## [1299] Sao Tome and Principe Sao Tome and Principe
## [1301] Sao Tome and Principe Sao Tome and Principe
## [1303] Sao Tome and Principe Sao Tome and Principe
## [1305] Sao Tome and Principe Sao Tome and Principe
## [1307] Sao Tome and Principe Sao Tome and Principe
## [1309] Saudi Arabia Saudi Arabia
## [1311] Saudi Arabia Saudi Arabia
## [1313] Saudi Arabia Saudi Arabia
## [1315] Saudi Arabia Saudi Arabia
## [1317] Saudi Arabia Saudi Arabia
## [1319] Saudi Arabia Saudi Arabia
## [1321] Senegal Senegal
## [1323] Senegal Senegal
## [1325] Senegal Senegal
## [1327] Senegal Senegal
## [1329] Senegal Senegal
## [1331] Senegal Senegal
## [1333] Serbia Serbia
## [1335] Serbia Serbia
## [1337] Serbia Serbia
## [1339] Serbia Serbia
## [1341] Serbia Serbia
## [1343] Serbia Serbia
## [1345] Sierra Leone Sierra Leone
## [1347] Sierra Leone Sierra Leone
## [1349] Sierra Leone Sierra Leone
## [1351] Sierra Leone Sierra Leone
## [1353] Sierra Leone Sierra Leone
## [1355] Sierra Leone Sierra Leone
## [1357] Singapore Singapore
## [1359] Singapore Singapore
## [1361] Singapore Singapore
## [1363] Singapore Singapore
## [1365] Singapore Singapore
## [1367] Singapore Singapore
## [1369] Slovak Republic Slovak Republic
## [1371] Slovak Republic Slovak Republic
## [1373] Slovak Republic Slovak Republic
## [1375] Slovak Republic Slovak Republic
## [1377] Slovak Republic Slovak Republic
## [1379] Slovak Republic Slovak Republic
## [1381] Slovenia Slovenia
## [1383] Slovenia Slovenia
## [1385] Slovenia Slovenia
## [1387] Slovenia Slovenia
## [1389] Slovenia Slovenia
## [1391] Slovenia Slovenia
## [1393] Somalia Somalia
## [1395] Somalia Somalia
## [1397] Somalia Somalia
## [1399] Somalia Somalia
## [1401] Somalia Somalia
## [1403] Somalia Somalia
## [1405] South Africa South Africa
## [1407] South Africa South Africa
## [1409] South Africa South Africa
## [1411] South Africa South Africa
## [1413] South Africa South Africa
## [1415] South Africa South Africa
## [1417] Spain Spain
## [1419] Spain Spain
## [1421] Spain Spain
## [1423] Spain Spain
## [1425] Spain Spain
## [1427] Spain Spain
## [1429] Sri Lanka Sri Lanka
## [1431] Sri Lanka Sri Lanka
## [1433] Sri Lanka Sri Lanka
## [1435] Sri Lanka Sri Lanka
## [1437] Sri Lanka Sri Lanka
## [1439] Sri Lanka Sri Lanka
## [1441] Sudan Sudan
## [1443] Sudan Sudan
## [1445] Sudan Sudan
## [1447] Sudan Sudan
## [1449] Sudan Sudan
## [1451] Sudan Sudan
## [1453] Swaziland Swaziland
## [1455] Swaziland Swaziland
## [1457] Swaziland Swaziland
## [1459] Swaziland Swaziland
## [1461] Swaziland Swaziland
## [1463] Swaziland Swaziland
## [1465] Sweden Sweden
## [1467] Sweden Sweden
## [1469] Sweden Sweden
## [1471] Sweden Sweden
## [1473] Sweden Sweden
## [1475] Sweden Sweden
## [1477] Switzerland Switzerland
## [1479] Switzerland Switzerland
## [1481] Switzerland Switzerland
## [1483] Switzerland Switzerland
## [1485] Switzerland Switzerland
## [1487] Switzerland Switzerland
## [1489] Syria Syria
## [1491] Syria Syria
## [1493] Syria Syria
## [1495] Syria Syria
## [1497] Syria Syria
## [1499] Syria Syria
## [1501] Taiwan Taiwan
## [1503] Taiwan Taiwan
## [1505] Taiwan Taiwan
## [1507] Taiwan Taiwan
## [1509] Taiwan Taiwan
## [1511] Taiwan Taiwan
## [1513] Tanzania Tanzania
## [1515] Tanzania Tanzania
## [1517] Tanzania Tanzania
## [1519] Tanzania Tanzania
## [1521] Tanzania Tanzania
## [1523] Tanzania Tanzania
## [1525] Thailand Thailand
## [1527] Thailand Thailand
## [1529] Thailand Thailand
## [1531] Thailand Thailand
## [1533] Thailand Thailand
## [1535] Thailand Thailand
## [1537] Togo Togo
## [1539] Togo Togo
## [1541] Togo Togo
## [1543] Togo Togo
## [1545] Togo Togo
## [1547] Togo Togo
## [1549] Trinidad and Tobago Trinidad and Tobago
## [1551] Trinidad and Tobago Trinidad and Tobago
## [1553] Trinidad and Tobago Trinidad and Tobago
## [1555] Trinidad and Tobago Trinidad and Tobago
## [1557] Trinidad and Tobago Trinidad and Tobago
## [1559] Trinidad and Tobago Trinidad and Tobago
## [1561] Tunisia Tunisia
## [1563] Tunisia Tunisia
## [1565] Tunisia Tunisia
## [1567] Tunisia Tunisia
## [1569] Tunisia Tunisia
## [1571] Tunisia Tunisia
## [1573] Turkey Turkey
## [1575] Turkey Turkey
## [1577] Turkey Turkey
## [1579] Turkey Turkey
## [1581] Turkey Turkey
## [1583] Turkey Turkey
## [1585] Uganda Uganda
## [1587] Uganda Uganda
## [1589] Uganda Uganda
## [1591] Uganda Uganda
## [1593] Uganda Uganda
## [1595] Uganda Uganda
## [1597] United Kingdom United Kingdom
## [1599] United Kingdom United Kingdom
## [1601] United Kingdom United Kingdom
## [1603] United Kingdom United Kingdom
## [1605] United Kingdom United Kingdom
## [1607] United Kingdom United Kingdom
## [1609] United States United States
## [1611] United States United States
## [1613] United States United States
## [1615] United States United States
## [1617] United States United States
## [1619] United States United States
## [1621] Uruguay Uruguay
## [1623] Uruguay Uruguay
## [1625] Uruguay Uruguay
## [1627] Uruguay Uruguay
## [1629] Uruguay Uruguay
## [1631] Uruguay Uruguay
## [1633] Venezuela Venezuela
## [1635] Venezuela Venezuela
## [1637] Venezuela Venezuela
## [1639] Venezuela Venezuela
## [1641] Venezuela Venezuela
## [1643] Venezuela Venezuela
## [1645] Vietnam Vietnam
## [1647] Vietnam Vietnam
## [1649] Vietnam Vietnam
## [1651] Vietnam Vietnam
## [1653] Vietnam Vietnam
## [1655] Vietnam Vietnam
## [1657] West Bank and Gaza West Bank and Gaza
## [1659] West Bank and Gaza West Bank and Gaza
## [1661] West Bank and Gaza West Bank and Gaza
## [1663] West Bank and Gaza West Bank and Gaza
## [1665] West Bank and Gaza West Bank and Gaza
## [1667] West Bank and Gaza West Bank and Gaza
## [1669] Yemen Rep. Yemen Rep.
## [1671] Yemen Rep. Yemen Rep.
## [1673] Yemen Rep. Yemen Rep.
## [1675] Yemen Rep. Yemen Rep.
## [1677] Yemen Rep. Yemen Rep.
## [1679] Yemen Rep. Yemen Rep.
## [1681] Zambia Zambia
## [1683] Zambia Zambia
## [1685] Zambia Zambia
## [1687] Zambia Zambia
## [1689] Zambia Zambia
## [1691] Zambia Zambia
## [1693] Zimbabwe Zimbabwe
## [1695] Zimbabwe Zimbabwe
## [1697] Zimbabwe Zimbabwe
## [1699] Zimbabwe Zimbabwe
## [1701] Zimbabwe Zimbabwe
## [1703] Zimbabwe Zimbabwe
## 142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe- Note at the end, we can see 142 levels (or categories). Let’s add this to our summary; how?
summary(factor(gapminder$country))## Afghanistan Albania Algeria
## 12 12 12
## Angola Argentina Australia
## 12 12 12
## Austria Bahrain Bangladesh
## 12 12 12
## Belgium Benin Bolivia
## 12 12 12
## Bosnia and Herzegovina Botswana Brazil
## 12 12 12
## Bulgaria Burkina Faso Burundi
## 12 12 12
## Cambodia Cameroon Canada
## 12 12 12
## Central African Republic Chad Chile
## 12 12 12
## China Colombia Comoros
## 12 12 12
## Congo Dem. Rep. Congo Rep. Costa Rica
## 12 12 12
## Cote d'Ivoire Croatia Cuba
## 12 12 12
## Czech Republic Denmark Djibouti
## 12 12 12
## Dominican Republic Ecuador Egypt
## 12 12 12
## El Salvador Equatorial Guinea Eritrea
## 12 12 12
## Ethiopia Finland France
## 12 12 12
## Gabon Gambia Germany
## 12 12 12
## Ghana Greece Guatemala
## 12 12 12
## Guinea Guinea-Bissau Haiti
## 12 12 12
## Honduras Hong Kong China Hungary
## 12 12 12
## Iceland India Indonesia
## 12 12 12
## Iran Iraq Ireland
## 12 12 12
## Israel Italy Jamaica
## 12 12 12
## Japan Jordan Kenya
## 12 12 12
## Korea Dem. Rep. Korea Rep. Kuwait
## 12 12 12
## Lebanon Lesotho Liberia
## 12 12 12
## Libya Madagascar Malawi
## 12 12 12
## Malaysia Mali Mauritania
## 12 12 12
## Mauritius Mexico Mongolia
## 12 12 12
## Montenegro Morocco Mozambique
## 12 12 12
## Myanmar Namibia Nepal
## 12 12 12
## Netherlands New Zealand Nicaragua
## 12 12 12
## Niger Nigeria Norway
## 12 12 12
## Oman Pakistan Panama
## 12 12 12
## (Other)
## 516Now what if we want to permanently save
countryas a factor.We might not need to do this in in a real case b/c w can always do on the fly if needed, but we could need to do it so we can alter levels (they could be ordinal).
Let’s add the factor version of
countryat the end of gapminder as a new column. How would you think we do that?
gapminder$countr_fac <- factor(gapminder$country)str(gapminder)## 'data.frame': 1704 obs. of 7 variables:
## $ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ pop : num 8425333 9240934 10267083 11537966 13079460 ...
## $ continent : chr "Asia" "Asia" "Asia" "Asia" ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ gdpPercap : num 779 821 853 836 740 ...
## $ countr_fac: Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...Notice the difference.
Ok let’s look at some helpful methods to inspect the data.
typeof(gapminder$year)## [1] "integer"typeof(gapminder$country)## [1] "character"str(gapminder$country)## chr [1:1704] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...str(gapminder$countr_fac)## Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...length(gapminder)## [1] 7typeof(gapminder)## [1] "list"- What’s a list?? a data structure can be thought of as a container of many different types of objects.
- All dataframes in R under the hood are list.
- But lists are important in R to be aware of b/c complex objects are lists
- This can include how statistical models are represented or plots
Let’s explore some other functions we can use to inspect dataframes:
nrow(gapminder)## [1] 1704ncol(gapminder)## [1] 7dim(gapminder)## [1] 1704 7colnames(gapminder)## [1] "country" "year" "pop" "continent" "lifeExp"
## [6] "gdpPercap" "countr_fac"head(gapminder)## country year pop continent lifeExp gdpPercap countr_fac
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453 Afghanistan
## 2 Afghanistan 1957 9240934 Asia 30.332 820.8530 Afghanistan
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007 Afghanistan
## 4 Afghanistan 1967 11537966 Asia 34.020 836.1971 Afghanistan
## 5 Afghanistan 1972 13079460 Asia 36.088 739.9811 Afghanistan
## 6 Afghanistan 1977 14880372 Asia 38.438 786.1134 AfghanistanSubsetting
What’s a column in a data frame, again?
Right, it’s a vector. Let’s pull out a column as a vector by itself and explore how we subset vectors.
life_exp <- gapminder[['lifeExp']]str(life_exp)## num [1:1704] 28.8 30.3 32 34 36.1 ...- let’s get the first element:
life_exp[1]## [1] 28.801- hwat about elements 3 & 7
life_exp[c(3,7)]## [1] 31.997 39.854- we use the
cfunction here - what about a series or sequence?
- first let’s see how to do one in R
1:4## [1] 1 2 3 4- Note this will expand to [1,2,3,4]
life_exp[1:4]## [1] 28.801 30.332 31.997 34.020- to skip we use negatives
life_exp[-1:4]Why didnt that work? Yes, -1:4 expands to -1,0,1,2,3,4
-1:4## [1] -1 0 1 2 3 4life_exp[-(1:4)]Also works:
life_exp[-c(1:4)]Data frames
- Bracket notation - if we use a single bracket with number, it will return
- Single column as a dataframe
head(gapminder[3])## pop
## 1 8425333
## 2 9240934
## 3 10267083
## 4 11537966
## 5 13079460
## 6 14880372str(gapminder[3])## 'data.frame': 1704 obs. of 1 variable:
## $ pop: num 8425333 9240934 10267083 11537966 13079460 ...However if we use [[3]] it’ll return the column as a vector
head(gapminder[[3]])## [1] 8425333 9240934 10267083 11537966 13079460 14880372head(gapminder[["lifeExp"]])## [1] 28.801 30.332 31.997 34.020 36.088 38.438str(gapminder[[3]])## num [1:1704] 8425333 9240934 10267083 11537966 13079460 ...A vector.
Think about it via this image:
Peeling onion
$ dollar sign can pull out a column by name. A lot easier to remember names than their numbers.
head(gapminder$year)## [1] 1952 1957 1962 1967 1972 1977We can pull out by rows and columns by using two arguments in []
gapminder[1:3,] #row 1-3 and all columns## country year pop continent lifeExp gdpPercap countr_fac
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453 Afghanistan
## 2 Afghanistan 1957 9240934 Asia 30.332 820.8530 Afghanistan
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007 Afghanistangapminder[3,] # ## country year pop continent lifeExp gdpPercap countr_fac
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007 Afghanistangapminder[3:10, 1:3]## country year pop
## 3 Afghanistan 1962 10267083
## 4 Afghanistan 1967 11537966
## 5 Afghanistan 1972 13079460
## 6 Afghanistan 1977 14880372
## 7 Afghanistan 1982 12881816
## 8 Afghanistan 1987 13867957
## 9 Afghanistan 1992 16317921
## 10 Afghanistan 1997 22227415Let’s subset gapminder and only include data from 87
gapminder[gapminder$year == 1987, ]How about population greater than 15,000,000
gapminder[gapminder$pop >= 15000000,]GGPLOT2 - 30min or less
- R-base has a plot function but less intuitive:
plot() - Ggplot2 should have been installed
But if not:
install.packages("ggplot2")library("ggplot2")- We will go over more of ggplot2 tomorrow.
- grammar of graphics - a way to express plots are layers of data and their visual expressions on a plot
- We select the data, map it to axis and tell ggplot how to represent it.
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point()
- we can transform the axis using a log10 scale
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point(alpha = 0.5) + scale_x_log10()
ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country, color=continent)) +
geom_line()
Make the points stand out:
ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country)) +
geom_line(mapping = aes(color=continent)) + geom_point()
ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country, color=continent)) +
geom_line() + geom_point()
americas <- gapminder[gapminder$continent == "Americas",] #subset
ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
geom_line() +
facet_wrap( ~ country) +
theme(axis.text.x = element_text(angle = 45))
You’ll get hands-on tomorrow with Stephanie. Thanks everyone!